--- license: cc-by-nc-sa-4.0 pretty_name: UAVLight task_categories: - image-to-image - image-to-3d tags: - computer-vision - novel-view-synthesis - 3d-reconstruction - gaussian-splatting - nerf - relighting - illumination-robustness - uav - outdoor-scenes - benchmark size_categories: - 1K.zip .zip ... metadata/ scenes.csv zip_sizes.csv file_list.txt zip_list.txt summary.txt ``` The `data/` directory contains scene-level zip archives. Each zip file corresponds to one UAV scene. The `metadata/` directory provides summary files describing the released scenes, archive sizes, and file lists. The `assets/` directory contains visual materials used by this dataset card. ## Scene Archive Structure After extracting a scene archive, the directory structure is: ```text / images/ sparse/ dense_points.ply downsampled_points.ply mesh.ply split.csv sun_directions.txt train_list.txt test_list.txt ``` For example: ```text 1121211223101030/ images/ sparse/ dense_points.ply downsampled_points.ply mesh.ply split.csv sun_directions.txt train_list.txt test_list.txt ``` ## File Descriptions ### `images/` This folder contains the multi-view RGB images for the scene. These images are the main visual observations used for reconstruction, novel-view synthesis, and benchmark evaluation. ### `sparse/` This folder contains sparse reconstruction files, such as camera poses and COLMAP-style sparse reconstruction outputs. These files can be used to initialize or evaluate reconstruction methods that rely on calibrated cameras. ### `split.csv` This file records the predefined split information for the scene. It can be used to identify which images belong to training and testing subsets. ### `train_list.txt` This file contains the list of training images used for scene reconstruction or model fitting. ### `test_list.txt` This file contains the list of testing images used for novel-view synthesis and benchmark evaluation. ### `sun_directions.txt` This file provides sun direction annotations associated with the scene/images. These annotations are useful for illumination-aware reconstruction, lighting transfer, relighting-related analysis, and evaluating robustness under outdoor lighting variation. ### `dense_points.ply` A dense point cloud reconstructed for the scene. This is provided as an optional geometry asset and may be useful for visualization, geometry analysis, or method initialization. ### `downsampled_points.ply` A downsampled version of the point cloud. This file is smaller and can be useful for quick visualization or lightweight processing. ### `mesh.ply` A reconstructed mesh for the scene. This is provided as an optional geometry asset and may be useful for visualization or geometry-related analysis. ## Metadata Files The `metadata/` directory contains several files to help users inspect and manage the dataset. ### `metadata/scenes.csv` A scene-level summary file. Each row corresponds to one scene and records whether the expected files are available, including image folders, sparse reconstruction files, geometry assets, sun direction annotations, and train/test split files. ### `metadata/zip_sizes.csv` A summary of all released scene archives and their file sizes. ### `metadata/file_list.txt` A full file list generated from the original packed dataset directory. ### `metadata/zip_list.txt` A list of all released scene-level zip archives. ### `metadata/summary.txt` A compact summary of the release, including the number of scenes, number of zip archives, and total compressed size. ## Download You can download the full dataset using the Hugging Face CLI: ```bash huggingface-cli download dukang92/UAVLight --repo-type dataset --local-dir UAVLight ``` Alternatively, you can download individual scene archives from the `data/` folder. For example, after downloading one scene archive: ```bash unzip data/.zip -d UAVLight_scenes/ ``` The extracted scene will follow the structure described above. ## Usage Example A typical workflow is: ```text 1. Download the dataset or selected scene archives. 2. Extract the scene zip files. 3. Use train_list.txt for reconstruction or model training. 4. Use test_list.txt for novel-view synthesis evaluation. 5. Use sparse/ camera files for pose information. 6. Optionally use sun_directions.txt for illumination-aware analysis. 7. Optionally use dense_points.ply, downsampled_points.ply, or mesh.ply for geometry visualization or initialization. ``` ## Intended Use UAVLight is intended for academic research on robust 3D reconstruction and novel-view synthesis in outdoor UAV scenes. Potential use cases include: - benchmarking illumination-robust reconstruction methods - evaluating Gaussian Splatting and NeRF-based methods under outdoor lighting variation - studying the effect of sunlight, shadows, and exposure variation on 3D reconstruction - developing lighting-aware scene representations - evaluating relighting or lighting-transfer consistency in reconstructed scenes ## Limitations UAVLight focuses on outdoor UAV scenes and illumination robustness. The dataset is not intended to cover all possible outdoor environments, weather conditions, or dynamic scene changes. Users should also note that geometry assets such as point clouds and meshes are provided as auxiliary reconstruction outputs and may not be perfect ground truth. ## License This dataset is released for non-commercial research use only under the license specified in this repository. ## Citation If you use UAVLight in your research, please cite: ```bibtex @inproceedings{du2026uavlight, title = {UAVLight: A Benchmark for Illumination-Robust 3D Reconstruction in Unmanned Aerial Vehicle (UAV) Scenes}, author = {Kang Du and Xue Liao and Junpeng Xia and Chaozheng Guo and Yi Gu and Yirui Guan and Duotun Wang and Sheng Huang and Zeyu Wang}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, year = {2026} } ``` ## Contact For questions about the dataset, please contact: **Kang Du** Email: kdu800@connect.hkust-gz.edu.cn